Forest condition anomaly index values covering Germany for 2016-2024

General description: In Lange et. al (2024) we utilised Sentinel-2 tree species-specific reflectance time series for extracting forest condition across Germany from 2016 to 2022. These time series' seasonal evolution - computed separately for seven natural regions - serves as reference when calculating a similarity metric – further called forest condition anomaly index (FCA). The FCA is computed between each single reflectance observation and the respective date within the reference time series, also considering the natural temporal deviations caused by phenology. FCA temporal aggregation allowed generating spatially comprehensive forest condition anomaly maps. FCA patterns in space and time are in line with dominant drivers like fires, storms and insect infestations and in agreement with state-of-the-art forest disturbance products using a threshold of FCA = −0.15 for forest loss. More information can be found in the related publication and in the UFZ Forest condition monitor web-application. Data description: Data is provided in GeoTiff format (projection EPSG:32632). Forest condition anomaly maps are available in a spatial resolution of 20 m for the years 2016 to 2024 as monthly (May to October) and yearly maps. Values are scaled by 10 000 to reduce the file size. Final FCA values are obtained by dividing the raw values by 10 000 and range from -1 to 1. A negative value generally indicates a poorer forest condition, for example, due to negative changes in chlorophyll or water content or due to crown defoliation. Through validation using forest surveys, data from the Copernicus Emergency Management System and other current maps of forest cover loss, it can be relatively accurate determined that a value below -0.15 indicates a heavily damaged or dead forest stand. Stronger damage (such as significant needle/leaf loss or tree mortality) is generally captured more precise than light damage (such as slight needle/leaf loss). Moderate forest condition values correspondingly show no anomaly and represent the expected normal condition for the respective tree species at the given time within the year. Positive forest condition values indicate a positive deviation from the expected state, which might stem from from positive chlorophyll or water content changes or from denser foliage or needle cover. File descriptions: Data is provided in zip archives containing maps in GeoTiff format (projection EPSG:32632). 4 zip files are provided: FCA_v0007-0006_Germany_2016-2024_yearly_R20m.zip contains 8 yearly FCA maps FCA_v0007-0006_Germany_2016-2019_monthly_R20m.zip contains 24 monthly maps (May to October for 2016 to 2019) FCA_v0007-0006_Germany_2020-2023_monthly_R20m.zip contains 24 monthly maps (May to October for 2020 to 2023) FCA_v0007-0006_Germany_2024_monthly_R20m.zip contains 6 monthly maps (May to October for 2024) Please note: Forest pixels were selected according to the tree species map from Blickensdörfer et al. (2024).总体说明：在Lange等人（2024）的研究中，我们利用哨兵-2（Sentinel-2）树种专属反射率时间序列，提取了2016至2022年德国全境的森林状况信息。我们针对7个自然分区分别计算了这些时间序列的季节演化特征，将其作为参考基准，用于计算相似度指标——即森林状况异常指数（Forest Condition Anomaly Index, FCA）。FCA的计算基于每一条单独的反射率观测值与参考时间序列中对应日期的基准值，同时考虑了物候导致的自然时间偏差。通过对FCA进行时间聚合，可以生成覆盖全域的森林状况异常地图。FCA的时空分布模式与火灾、风暴、虫害等主要驱动因子的影响高度吻合，且与现有前沿森林干扰产品的结果一致，研究中采用FCA=-0.15作为森林损失的阈值。更多详细信息可参阅相关学术论文及UFZ森林状况监测Web应用程序。 数据说明：本数据集采用GeoTiff格式存储（投影坐标系为EPSG:32632）。2016至2024年的森林状况异常地图以20米空间分辨率提供，包含月度（5月至10月）与年度两种类型的地图。为压缩文件体积，数据值已按10000倍缩放，原始FCA值可通过将缩放后的值除以10000得到，取值范围为-1至1。负值通常代表森林状况较差，例如叶绿素或含水量下降、树冠落叶等情况。通过森林调查数据、哥白尼应急管理系统（Copernicus Emergency Management System）数据及其他现有森林覆盖损失地图进行验证，可相对准确地判定：FCA值低于-0.15代表该林分遭受严重损伤或已死亡。相较于轻度损伤（如轻微落叶/针叶脱落），重度损伤（如显著针叶/叶片脱落或树木死亡）的识别精度更高。中等的FCA值则表示无异常，对应树种在该年度特定时段的正常生长状态。正值代表森林状况偏离基准状态且表现更优，可能源于叶绿素或含水量提升、冠层更茂密等原因。 文件说明：数据以压缩归档包形式提供，内部包含GeoTiff格式的地图文件（投影坐标系为EPSG:32632），共提供4个压缩包： FCA_v0007-0006_Germany_2016-2024_yearly_R20m.zip：包含8张年度FCA地图 FCA_v0007-0006_Germany_2016-2019_monthly_R20m.zip：包含24张月度地图（2016至2019年的5月至10月） FCA_v0007-0006_Germany_2020-2023_monthly_R20m.zip：包含24张月度地图（2020至2023年的5月至10月） FCA_v0007-0006_Germany_2024_monthly_R20m.zip：包含6张月度地图（2024年的5月至10月） 注意事项：森林像元的选取依据Blickensdörfer等人（2024）发布的树种地图。